A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends

Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing

Modelling the flow properties of rubber blends makes it possible to predict their rheological behaviour during the processing and production of rubber-based products. As the nonlinear nature of such complex processes complicates the creation of exact analytical models, it is appropriate to use artificial intelligence tools in this modelling. The present study was implemented to develop a highly efficient artificial neural network model, optimised using a novel training algorithm with fast parallel computing to predict the results of rheological tests of rubber blends performed under different conditions. A series of 120 real dynamic viscosity-time curves, acquired by a rubber process analyser for styrene-butadiene rubber blends with varying carbon black contents vulcanised at different temperatures, were analysed using a Generalised Regression Neural Network. The model was optimised by limiting the fitting error of the training dataset to a pre-specified value of less than 1%. All repeated calculations were made via parallel computing with multiple computer cores, which significantly reduces the total computation time. An excellent agreement between the predicted and measured generalisation data was found, with an error of less than 4.7%, confirming the high generalisation performance of the newly developed model.

Data driven of underground water level using artificial intelligence hybrid algorithms

As the population grows, industry and agriculture have also developed and water resources require quantitative and qualitative management. Currently, the management of water resources is essential in the exploitation and development of these resources. For this reason, it is important to study water level fluctuations to check the amount of underground water storage. It is vital to study the level of underground water in Khuzestan province with a dry climate. The methods which exist for predicting and managing water resources are used in studies according to their strengths and weaknesses and according to the conditions. In recent years, artificial intelligence has been used extensively for groundwater resources worldwide. Since artificial intelligence models have provided good results in water resources up to now, in this study, the hybrid model of three new recombined methods including FF-KNN, ABC-KNN and DL-FF-KNN-ABC-MLP has been used to predict the underground water level in Khuzestan province (Qale-Tol area). The novelty of this technique is that it first does classification by presenting the first block (combination of FF-DWKNN algorithm) and predicts with the second block (combination of ABC-MLP algorithm). The algorithm's ability to decrease data noise will be enabled by this feature. In order to predict this key and important parameter, a part of the data related to wells 1-5 has been used to build artificial intelligence hybrid models and also to test these models, and to check this model three wells 6-8 have been used for the development of these models. After checking the results, it is clear that the statistical RMSE values of this algorithm including test, train and total data are 0.0451, 0.0597 and 0.0701, respectively. According to the results presented in the table reports, the performance accuracy of DL-FF-KNN-ABC-MLP for predicting this key parameter is very high.

Application of Generalized Regression Neural Network and Gaussian Process Regression for Modelling Hybrid Micro-Electric Discharge Machining: A Comparative Study

Micro-Electric Discharge Machining (μ-EDM) is one of the widely applied micromanufacturing processes. However, it has several limitations, such as a low cutting rate, difficult debris removal, and poor surface integrity, etc. Hybridization of the μ-EDM is proposed as an alternative to overcome the process limitations. Conversely, it complicates the process nature and poses a challenge for modelling and predicting critical process responses. Therefore, in this work, two distinct, nonparametric, previously unreported, workpiece material independent models using a Generalized Regression Neural Network (GRNN) and Gaussian Process Regression (GPR) were developed and compared to assess their performance with limited training data. Various smoothing factors and kernels were tested for GRNN and GPR, respectively. The prediction of models was compared in terms of the mean absolute percentage error, root mean square error, and coefficient of determination. The results showed that GPR outperforms GRNN and accurately predicts the μ-EDM process responses. The GRNN’s performance was better for less stochastic output with a discernible pattern than other outputs. The Automatic Relevance Determination (ARD) squared exponential kernel was found to be the best performing kernel among those chosen. GPR models can be used with reasonable accuracy to predetermine critical process outputs as they have R2 values above 0.90 for both training and validation data for all outputs. This work paves the way for future industrial implementation of GPR to model and predict the outputs of complex hybrid machining processes.